Detection Method of Freshness of Penaeus Vannamei Based on
Hyperspectral
ZHU Chen-guang1, LIU Ya-jun2, LI Xin-xing1, 3, GONG Wei-wei4*, GUO Wei1
1. Beijing Laboratory of Food Quality and Safety, College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China
2. Chengde No. 8 Middle School, Chengde 067000, China
3. Energy and Environment Engineering Institute, Nanchang Institute of Technology, Nanchang 330044, China
4. Transportation and Economic Research Institute of China Academy of Railway Sciences Group Corporation Limited, Beijing 100081, China
Abstract：In this study, Penaeus vannamei was taken as the research object to explore an efficient, rapid and non-destructive freshness detection method. Total volatile basic nitrogen（TVB-N）is an important chemical index to judge the freshness of shrimp. However, the traditional method is time-consuming and labor-consuming, which limits the real-time detection of large quantities. In recent years, hyperspectral technology has been an analysis technology integrating image and spectral information. Each pixel in the hyperspectral image contains the spectral information of the whole band. This technology has become a technology of meat freshness detection. This study collected 860~1 700 nm hyperspectral data of Penaeus vannamei samples for 8 consecutive days. After removing the abnormal samples, 150 groups of test samples were determined. We collected 254-dimensional spectral data in each group. The original hyperspectral image was corrected in black and white, and ENVI software extracted the spectral data from the hyperspectral image. We ensured the corresponding relationship between the extracted spectral data and the TVB-N index. The average spectrum of the ROI is calculated to obtain the spectral data matrix, which is converted into ASCII code and saved. At the same time, the true value of TVB-N was obtained by the Kjeldahl method. In order to reduce the interference of water content of environment and shrimp surface and effectively eliminate the irrelevant information and noise, this study used a multiple scattering correction algorithms to preprocess the shrimp hyperspectral and selected seven sensitive bands. Finally, a quantitative prediction model of TVB-N of Penaeus vannamei was established based on 120 training set samples and 30 validation set samples. We compared the model of BPNN, RBFNN and PCA. The r and NRMSE of the BPNN model were 0.902 1 and 0.214 0, the RBFNN model were 0.868 3 and 0.223 0, PCR model were 0.757 6 and 0.390 0, respectively. The results showed that the MSC-BPNN model had the best prediction effect, and there is a close correlation between hyperspectral reflectance and freshness of Penaeus vannamei. This paper supports the freshness detection of shrimp based on spectral technology.
Key words：Hyperspectral; Freshness; Multivariate correction scattering; BP neural network; Radial basis function neural network
朱晨光，刘亚军，李鑫星，宫薇薇，郭 渭. 凡纳滨对虾新鲜度的高光谱检测方法[J]. 光谱学与光谱分析, 2023, 43(01): 107-110.
ZHU Chen-guang, LIU Ya-jun, LI Xin-xing, GONG Wei-wei, GUO Wei. Detection Method of Freshness of Penaeus Vannamei Based on
Hyperspectral. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(01): 107-110.
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